The journey to AI is easier than you might think

Attempting an epic hike or road trip across unfamiliar territory without an expert guide, detailed map or plenty of supplies will certainly run the risk of failure.

So why would you approach an investment in AI without the right strategy and a prescriptive approach?

The urge to make the move to AI is strong and justifiable. According to research cited in MIT Sloan’s global study of AI early adopters, almost 85 percent believe AI will allow their companies to obtain or sustain a competitive advantage.In many cases, market leadership is at stake. In other cases, it’s about pure survival. At the core is the ability to unlock the value exponentially growing amount of data.

Getting started isn’t easy, because no matter how sophisticated your machine-learning algorithms, they’re only as good as the data that feeds them. As the MIT Sloan report states, “bad data is paralyzing [to AI].”

Our experience with early adopters confirms that clients are entering their journey from various starting points and skill levels, but we’re seeing some similar issues emerge:

1. Enabling skills. There’s an incredible battle going on out there to obtain the skills for data architects, data scientists and AI developers. Have you adopted repeatable best practices how to build AI models based on open standards? Have you deployed easy-to-use tools and services that empower more and more of their workforce to contribute to the new world of AI-driven businesses?

2. Preparing data. It’s essential to make your data ready for AI as it’s your data that fuels AI models. Is your data architected for dynamic AI training and use cases? Is it business-ready? Do you trust the integrity of your data? Are you meeting the new compliance and privacy policies that the AI era will bring?

3. Trusting AI outcomes. Inthe end, you must trust what your AI models are telling you and recommending you do. Do you have transparency in how outcomes are formed? Can you explain what a certain model is telling you? How sure are you that there are no inherent biases in your models that will skew outcomes? Can AI processes and outcomes be easily audited?

The ladder to AI explained:

VIDEO

The ladder to AI

At the heart of it, success with AI is going to depend on an organization’s ability to apply an architected multicloud model and approach to how it accelerates toward AI through mastering four levels: collecting data, organizing data, analyzing data with AI everywhere and infusing AI-driven business processes. It’s this type of model that has differentiated the successful from the struggling among AI early adopters, and their ability to address the three core challenges outlined above.

IBM has systematically learned from our data & AI customers and has built out a prescriptive, architected framework is designed to help our clients’ journey to AI. It starts with information architecture for AI, but recognizes that different clients have different starting points and paths to success, all coming from different background of skills, tools and infrastructure. That’s why we’ve created a flexible way for them to move toward AI with better guidance on what offerings make the most sense to use for different use cases.

You’ll sometimes hear us refer to this organizational framework as the “ladder to AI.”

Clients can use products from each rung of the ladder, self-assemble and integrate them into workflows themselves, or they can go “all in” and purchase ICP for Data, a modernized information architecture that unifies and integrates all the capacities into one platform. Clients can build out ICP for Data for some projects, and use individual products such as Db2, Watson Studio, Cognos and OpenScale — without ICP for Data.